One of the disappointing aspects of the post-genomic era is that whilst a plethora of putative biomarkers have undergone preliminary clinical evaluations, only a small minority have received regulatory approval for clinical use from agencies such as the US Food & Drug Administration (FDA). This is evident from the small number of clinical markers currently used in breast cancer. Although the sequencing of the human genome is likely to have a profound influence on public health in the long-term, there have not as yet been a large number of practical advances regarding the development of new biomarkers based on this information. This has led to a concern that the level of investment in research is not being reflected in improved clinical outcomes, and there is particular concern that the benefits from the ‘genetic revolution’ have been slow to arrive. This apparent bottleneck in transfer from putative biomarker discovery to clinical application is primarily down to a lack of rigorous validation of emerging biomarkers.
In the field of medical diagnostics including oncology, the detection, identification, quantitation and characterization of cells of interest, such as cancer cells, through testing of biological specimens is an important aspect of diagnosis. In aiding a clinician in the diagnosis of cancer, a pathologist faces two key problems. Firstly, the pathologist must determine whether a tissue or cell sample removed from a patient is benign or malignant. Secondly, upon reaching a determination that the tissue or cell sample is malignant, the pathologist must then classify the aggressiveness of the cancer and determine its clinical and biological behavior.
A diagnosis of cancer must be confirmed through histological examination of a tissue or a cell sample removed from a patient. Such histological examination entails tissue-staining procedures that allow the morphological features of the tissue to be readily examined under a light microscope. The pathologist, after having examined the stained tissue or cell sample, makes qualitative determinations of the state of the tissue or the patient from whom the sample was removed and whether the tissue is benign or malignant. The aggressiveness of the tumour, however, is difficult to ascertain using standard histological techniques. The clinician uses the pathologist's histological analysis to select a suitable treatment, balancing the resistance or responsiveness of the cancer to therapy with the potential harm to the patient resulting from the selected therapy (Muss et al., 1994, N. Engl. J. Med. 330: 1260-66).
In the past, the examination of biological specimens has been performed manually by either a laboratory technician or a pathologist. In the manual method, a slide prepared with a biological specimen is viewed at a low magnification under a microscope to visually locate candidate cells of interest. Those areas of the slide where cells of interest are located are then viewed at a higher magnification to confirm those objects as cells of interest, such as tumour or cancer cells. The manual method is a tedious, time consuming subjective and often variable process to which only limited statistical confidence can be assigned due to inherent intra- and inter-observer variability [31, 32].
In the manual method, Immunohistochemistry (IHC) performed on formalin fixed tissue sections is the most commonly used assay and has replaced other biochemical-based methods using cell suspensions, which consisted of a mixture of normal and malignant tissues. In this regard, IHC based receptor analysis enables assessment of the tissue architecture and is also applicable on small tumours, which were often not suitable for biochemistry based assays. By way of example, hormone receptor status is routinely evaluated in all resected primary breast cancer tumours to assess the levels of Estrogen Receptor (ER) and Progesterone Receptor (PR). Currently, hormone receptor status is manually assessed by a pathologist and an arbitrary cut off of 10% positive cells (regardless of intensity) is used to decide whether a patient should have adjuvant hormonal therapy. Such an arbitrary cut off can lead to significant intra-observer variability.
Although there has been a concerted effort to improve IHC by the use of external quality assurance schemes, there is no equivalent check on those undertaking assessment. Whilst most of the known IHC histological scoring methods have been shown to correlate with clinical outcome when used by experienced pathologists [16, 17] an inherent intra- and inter-observer variability is a significant problem. For example, one study of 172 German pathologists highlighted the difficulties that can arise with manual interpretation, with 24% of ER staining interpreted as being falsely negative [18]. Thus IHC continues to lag behind in two key areas, which are: (i) interpretation and analysis of the stained target protein and (ii) accurate quantification of signal.
Some investigators believe that the solution to the problem of interpretation may be found in improved methods of image analysis [19-22]. Image analysis offers the potential to develop objective automated linear quantitative scoring models for IHC. A move away from the semi-quantitative manual scoring models currently employed would lead to less variability in results, increased throughput and the identification of new prognostic subgroups, which may not have been evident following initial manual analysis.
Accordingly, more recently, the visual examination of tissue and cell samples is often augmented by the use of a semi-automated (computer-aided) image analysis system. A representative system includes a computer that receives a magnified image of the tissue or cell sample from a television camera and processes the received optical image. Image analysis is generally used to assess the affinity of stains, such as IHC stains, for various biological markers.
The coupling of affinity staining and computer-aided image analysis has permitted clinicians to better select optimal therapies for their patients (such as, for example, hormone therapy for cancers that are ER and PR positive and anti-oncogene receptor therapy, such as using monoclonal antibodies directed against HER-2/neu (Herceptin), Epidermal Growth Factor Receptor (EGFR), or C225, alone or in combination with chemotherapy). In addition, image analysis techniques can be used to quantitate other receptors such as those in the erbB receptor family (HER-1, HER-2/neu, HER-3, and HER-4), their ligands (EGF, NDF, and TGFa), and downstream signals (PI3 kinase, Akt, MAP kinase, and JUN kinase).
Indeed, most of the high throughput work to date on image analysis of IHC of breast cancer receptor, such as ER, PR and HER2, has concentrated on the use of tissue microarrays (TMAs). With the advent of TMAs and high throughput pathology, new demands have been placed on the quality, reproducibility and accuracy of this high throughput platform, including the standardisation of interpretation for affinity stained biological specimens. Some of these approaches/systems have become redundant, while others allow (through use of a semi-automated image analysis approach) an increased sensitivity in relation to scoring of staining intensity.
One problem associated with the foregoing semi-automated systems is the difficulty in distinguishing between specifically stained (that is, positively stained) cells of interest, non-specifically stained (that is, negatively stained) cells of interest and background cells which are not of interest. By way of example, many semi-automated systems appear to have difficulty in distinguishing between negative tumour nuclei from stromal tissue and nuclei from lymphocytic infiltrate when assessing the level of ER and/or PR in biological samples.
Another problem associated with the semi-automated approach is the requirement for a manual calibration of the system to determine the morphologic and/or colorimetric features and/or patterns of the candidate cells of interest. This manual calibration step requires the continued need for operator input to initially locate candidate objects of interest for analysis. Such continued dependence on a manual input can lead to errors including cells of interest being missed. Such errors can be critical especially in assays for so-called rare events, such as, for example, finding one tumour cell in a cell population of one million normal cells. In certain rare event detection applications, it is important not to have false negatives. That is, it is important not to miss groups of disease specific cells, such as tumour cells clusters, despite their sparse occurrence and difficulty of detection.
For certain disease-specific states, the semi-automated approach also requires the intervention of an expert to distinguish between abnormal and normal cells. Differentiation based on some morphological features associated with certain disease specific cells, such as, for example, tumour cells, is subtle and requires an experienced pathologist for detection. Consequently, a prior knowledge of or an intensive training to identify the morphological features of the candidate cells and background stroma or epithelium cells is required to run the image analysis methods and systems implementing such methods which are disclosed in the art. By way of example, WO 2004/079636 (Aperio) discloses a semi-automated image analysis method and a system implementing the method which require both a training stage (by, for example, an expert in the biological sciences) and a pattern recognition stage.
A semi-automated image analysis approach which uses a “supervised” training step can be extremely time consuming because it requires a high degree of training to identify and/or quantify candidate cells of interest. This is not only true for tumour cell detection, but also for other applications ranging from neutrophil alkaline phosphatase assays, reticulocyte counting and maturation assessment, and others. The associated manual labor leads to a high cost for these procedures in addition to the potential errors that can arise from long, tedious manual examinations.
A further problem associated with the foregoing semi-automated systems is linked to the high degree of variability in respect to disease specific patterns across different patient samples. The currently available image analysis methods and systems implementing such methods often fail to reproduce the same results across different patient samples because they fail to allow for disease specific heterogeneity, such as tumour cell heterogeneity, on a patient by patient basis. In some instances, non-specific disease patterns are often misinterpreted as disease specific patterns. Indeed, one of the arguments leveled at TMAs is that they may not give a true representation of target biomarker that may have a heterogeneous pattern of expression across different patient samples.
Although TMA staining techniques have provided a considerable advantage in speeding up molecular assaying, the analysis of such results continues to be time-consuming and may be subject to more increased error than other types of assay systems. What is needed, therefore, is an improved image analysis method and system implementing such a method, which would provide for the rapid assay of a microarray so that the advantages of bulk microarray treatment techniques can be fully realized. If the true potential of TMAs is to be fulfilled, an improved image analysis method to rapidly and precisely quantify the expression of candidate biomarkers of interest within each tissue sample is required.
A need exists, therefore, for an improved image analysis method and an automated system for implementing the image analysis method which eliminates the need for operator input to locate and identify candidate objects of interest in a biological sample for analysis.
A need also exists for an improved automated image analysis method and a system for implementing the image analysis method which can quickly and accurately scan large amounts of biological material on a slide and provide an accurate measurement of the level of expression of one or more candidate objects of interest in a biological sample. Crucially, there is a need to develop approaches that can accurately quantify biomarker expression at different subcellular levels, be that at a nuclear, cytoplasmic and/or membraneous level.
The development of such methods and systems implementing such methods would have wide application in the treatment of diseases, such as cancer.